[Remote] AI Research Engineer

EnCharge AI is hiring an experienced AI Research Engineer to optimize deep learning models for deployment on edge AI platforms. The role focuses on model compression, quantization-aware training, and efficient inference techniques. You will work with PyTorch, ONNX Runtime, TVM, and collaborate with hardware engineers on edge AI chips. This is a remote position open to candidates in the USA.

• Research and develop quantization-aware training (QAT) and post-training quantization (PTQ) techniques for deep learning models
• Implement low-bit precision optimizations (e.g., INT8, BF16)
• Design and optimize efficient inference algorithms for AI workloads, focusing on latency, memory footprint, and power efficiency
• Work with frameworks such as PyTorch, ONNX Runtime, and TVM to deploy optimized models
• Analyze accuracy trade-offs and develop calibration techniques to mitigate precision loss in quantized models
• Collaborate with hardware engineers to optimize model execution for edge devices and NPUs
• Contribute to research on knowledge distillation, sparsity, pruning, and model compression techniques
• Benchmark performance across different hardware and software stacks
• Stay updated with the latest advancements in AI efficiency, model compression, and hardware acceleration